An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography.

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-target activity. However, there is still room for improvement. Here, we proposed a hybrid neural network named CrnnCrispr, which integrates a convolutional neural network and a recurrent neural network for on-target activity prediction. We performed unbiased experiments with four mainstream methods on nine public datasets with varying sample sizes. Additionally, we incorporated a transfer learning strategy to boost the prediction power on small-scale datasets. Our results showed that CrnnCrispr outperformed existing methods in terms of accuracy and generalizability. Finally, we applied a visualization approach to investigate the generalizable nucleotide-position-dependent patterns of sgRNAs for on-target activity, which shows potential in terms of model interpretability and further helps in understanding the principles of sgRNA design.

scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.

MOTIVATION: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. RESULTS: Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. AVAILABILITY AND IMPLEMENTATION: The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

Investigation of Tribological Behavior and Lubrication Mechanisms of Zinc Oxide under Poly α-olefin Lubrication Enhanced by the Electric Field.

The electric field induces complex effects on the tribological properties of zinc oxide (ZnO) under lubricated conditions, particularly at the nanoscale, where the friction process and mechanism remain unclear. In this paper, the tribological behaviors of ZnO under the lubrication of poly α-olefins (PAO) were investigated by molecular dynamics (MD) simulations with reactive force field (ReaxFF). The results reveal a significant enhancement in the tribological performances of ZnO with the application of the electric field, resulting in a 58.6% reduction in the coefficient of friction (COF) from 0.193 at 0 V/Å to 0.080 at 0.1 V/Å. This improvement can be attributed to the weakening of interfacial interaction, evidenced by a reduction in the number of C-O covalent bonds under the influence of the electric field, along with the formation of an adsorption film due to applied load and shear effects. Notably, the effect of the electric field and applied load extends the impact of interface slip on the tribological performance of ZnO. Overall, this study provides a comprehensive understanding of the impact of the electric field on reducing the friction of ZnO-based structured models, shedding light on explaining their tribological properties and lubrication mechanisms.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. METHODS: We included patients diagnosed with CRSwNP between January 1, 2020, and April 31, 2023. The endotype of patients with CRSwNP in this study was classified as eosinophilic or non-eosinophilic. Sinus CT images (29,993 images) were retrospectively collected, including the axial, coronal, and sagittal planes, and randomly divided into training, validation, and testing sets. A residual network-18 was used to construct the deep learning model based on these images. Loss functions, accuracy functions, confusion matrices, and receiver operating characteristic curves were used to assess the predictive performance of the model. Gradient-weighted class activation mapping was performed to visualize and interpret the operating principles of the model. RESULTS: Among 251 included patients, 86 and 165 had eosinophilic or non-eosinophilic CRSwNP, respectively. The median (interquartile range) patient age was 49 years (37-58 years), and 153 (61.0%) were male. The deep learning model showed good discriminative performance in the training and validation sets, with areas under the curves of 0.993 and 0.966, respectively. To confirm the model generalizability, the receiver operating characteristic curve in the testing set showed good discriminative performance, with an area under the curve of 0.963. The Kappa scores of the confusion matrices in the training, validation, and testing sets were 0.985, 0.928, and 0.922, respectively. Finally, the constructed deep learning model was used to predict the endotype of all patients, resulting in an area under the curve of 0.962. CONCLUSIONS: The deep learning model developed in this study may provide a novel noninvasive method for rhinologists to evaluate endotypes in patients with CRSwNP and help develop precise treatment strategies.

Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT.

Off-target effects of CRISPR/Cas9 can lead to suboptimal genome editing outcomes. Numerous deep learning-based approaches have achieved excellent performance for off-target prediction; however, few can predict the off-target activities with both mismatches and indels between single guide RNA (sgRNA) and target DNA sequence pair. In addition, data imbalance is a common pitfall for off-target prediction. Moreover, due to the complexity of genomic contexts, generating an interpretable model also remains challenged. To address these issues, firstly we developed a BERT-based model called CRISPR-BERT for enhancing the prediction of off-target activities with both mismatches and indels. Secondly, we proposed an adaptive batch-wise class balancing strategy to combat the noise exists in imbalanced off-target data. Finally, we applied a visualization approach for investigating the generalizable nucleotide position-dependent patterns of sgRNA-DNA pair for off-target activity. In our comprehensive comparison to existing methods on five mismatches-only datasets and two mismatches-and-indels datasets, CRISPR-BERT achieved the best performance in terms of AUROC and PRAUC. Besides, the visualization analysis demonstrated how implicit knowledge learned by CRISPR-BERT facilitates off-target prediction, which shows potential in model interpretability. Collectively, CRISPR-BERT provides an accurate and interpretable framework for off-target prediction, further contributes to sgRNA optimization in practical use for improved target specificity in CRISPR/Cas9 genome editing. The source code is available at https://github.com/BrokenStringx/CRISPR-BERT.

An electrochemiluminescence immunosensor based on signal magnification of luminol using OER-activated NiFe2O4@C@CeO2/Au as effective co-reaction accelerator.

In this work, a novel, label-free electrochemiluminescence (ECL) immunosensor was constructed for the ultrasensitive detection of carbohydrate antigen 15-3 (CA15-3) by the combined use of NiFe2O4@C@CeO2/Au hexahedral microbox and luminol luminophore. The synthesis of the co-reaction accelerator (NiFe2O4@C@CeO2/Au) was related to the calcination of FeNi-based metal-organic framework (MOF), as well as the ingrowth of CeO2 nanoparticles and modification of Au nanoparticles. To be specific, the electrical conductivity will be boosted due to the Au nanoparticles, the synergetic effect generated between CeO2 and calcination FeNi-MOF could offer better activity of oxygen evolution reaction (OER). Herein, the NiFe2O4@C@CeO2/Au hexahedral microbox as a co-reaction accelerator has excellent OER activity and production of reactive oxygen species (ROS), thus increasing the ECL intensity of luminol in a neutral medium without other co-reactants such as H2O2. Because of these benefits, the constructed ECL immunosensor was applied to detect CA15-3 as an example under optimum conditions, the designed ECL immunosensor exhibited high-level selectivity and sensitivity for CA15-3 biomarker within a linear response range of 0.01-100 U mL-1 and an ultralow detection limit of 0.545 mU mL-1 (S/N = 3), demonstrating its potentially valuable application in the area of clinical analysis.

Novel design constructed In2S3@SnO2 hollow heterojunctions by insufficiently etched MOFs as framework for photoelectrochemical bioanalysis.

Compared to sufficiently etched MOFs materials, insufficiently etched MOFs materials tend to display unsatisfactory performance due to their immature structure and have been eliminated from scientific research. Herein, this work reported a novel In2S3@SnO2 heterojunction (In2S3@SnO2-HSHT) materials, which were stably synthesized in high temperature aqueous environment and equipped extraordinary photoelectrochemical (PEC) properties, fabricated by a succinct hydrothermal synthesis method using insufficiently etched MIL-68 as a self-sacrificing template. Compared with the control groups and In2S3@SnO2 heterojunctions with collapse morphology synthesized by sufficiently etched MIL-68 in high temperature aqueous environment, In2S3@SnO2-HSHT synthesized from insufficiently etched MIL-68 as a template had a massively enhanced light-harvesting capability and generated more photoinduced charge carriers due to its well-preserved hollow structure. Therefore, based on outstanding PEC performance of In2S3@SnO2-HSHT, the established PEC label-free signal-off immunosensor to detect CYFRA 21-1, revealing vivid selectivity, stability, and reproducibility. This novel strategy adopted the insufficient chemical etching method neglected by the mainstream chemical etching approaches, which solved the challenge that the stability of the sufficient etched MOFs with hollow structure cannot be maintained under the subsequent high temperature aqueous reaction conditions, and was further applied to the design of hollow heterojunction materials for photoelectrochemical fields.

A sensitive electrochemiluminescence immunosensor for the detection of CA15-3 based on CeO2/Pt/rGO as a novel co-reaction accelerator.

In this work, we successfully constructed a label-free electrochemiluminescence (ECL) immunosensor for the detection of breast cancer marker antigen (CA15-3). In particular, 3,4,9,10-perylenetetracarboxylic acid (PTCA) is cleverly attached to the surface of silica spheres as a luminophore (NH2-SiO2-PTCA), which greatly alleviates the disadvantage of PTCA anti-induced aggregated luminescence and improves the ECL performance. Furthermore, Pt nanoparticles were used to dope CeO2 and introducing reduced graphene oxide (rGO) to prepare CeO2/Pt/rGO composites as a novel co-reaction accelerator. Among them, Pt nanoparticles were used to improve the electrical conductivity of CeO2, and the use of rGO as a substrate allows for a more uniform dispersion of CeO2 to increase the catalytic surface area, which effectively improves the performance of the co-reaction accelerator and thus increasing the ECL intensity of the PTCA/S2O82- system. Under the optimal conditions, the designed ECL immunosensor showed satisfactory results in the determination of CA15-3 with a linear range of 12.00 mU mL-1 - 120.00 U mL-1 and a low detection limit of 1.348 mU mL-1. Importantly, the resulting biosensor has good stability, high sensitivity and reliable reproducibility, suggesting its potential application in clinical research.

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction.

Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) often takes prolonged saturation duration (Ts) and relaxation delay (Td) to reach the steady state, and yet the insufficiently long Ts and Td in actual experiments may underestimate the CEST measurement. In this study, we aimed to develop a deep learning-based model for quasi-steady-state (QUASS) prediction from non-steady-state CEST acquired in experiments, therefore overcoming the limitation of the CEST effect which needs prolonged saturation time to reach a steady state. To support network training, a multi-pool Bloch-McConnell equation was designed to derive wide-ranging simulated Z-spectra, so as to solve the problem of time and labor consumption in manual annotation work. Following this, we formulated a hybrid architecture of long short-term memory (LSTM)-Attention to improve the predictive ability. The multilayer perceptron, recurrent neural network, LSTM, gated recurrent unit, BiLSTM, and LSTM-Attention were included in comparative experiments of QUASS CEST prediction, and the best performance was obtained by the proposed LSTM-Attention model. In terms of the linear regression analysis, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE), the results of LSTM-Attention demonstrate that the coefficient of determination in the linear regression analysis was at least R2 = 0.9748 for six different representative frequency offsets, the mean values of prediction accuracies in terms of SSIM, PSNR and MSE were 0.9991, 49.6714, and 1.68 × 10-4 for all frequency offsets. It was concluded that the LSTM-Attention model enabled high-quality QUASS CEST prediction.

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets.

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome.

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

A highly sensitive photoelectrochemical biosensor for CEA analysis based on hollow NiS@NiO/TiO2 composite with typal p-n heterostructure.

Heterostructured construction is regarded as a valuable approach to improve photoelectrochemical (PEC) performances. Herein, porous hollow NiS@NiO spheres were prepared derived from the Ni(TCY) MOFs precursor. Photoactive TiO2 was coupled with as-prepared NiS@NiO to form a close heterojunction interface of NiS@NiO/TiO2. NiS@NiO/TiO2 modified ITO electrode (NiS@NiO/TiO2/ITO) displayed fiercely enhanced photocurrent response, which was 4687-fold than that of NiS@NiO/ITO (0.008 µA) and 8.5-fold than that of TiO2/ITO (4.41 µA), respectively. Remarkable PEC property could be ascribed to the hollow NiS@NiO spheres with thin-shell structure provided there is a larger active surface area for harvesting the visible light. Most importantly, the p-n type NiS@NiO/TiO2 heterojunction could lead to generating more photo-excited charge carriers (e-/h+) and efficiently hinder the recombination of carriers, resulting in significantly augmented photocurrent output. Based on this outstanding PEC property, NiS@NiO/TiO2/ITO electrode fabricated sensing platform (BSA/anti-CEA/NiS@NiO/TiO2/ITO, BSA=Bovine serum albumin) exhibited high sensitivity for monitoring CEA (Carcinoembryonic antigen). Wide linear detection range was from 0.001 to 45 ng mL-1 and with a low detection limit of 1.67 × 10-4 ng mL-1 (S/N = 3). Prepared biosensors also showed good reproducibility, stability and had satisfying specificity. Thus, the proposed NiS@NiO/TiO2 heterostructured composite afforded well-design and synthesis strategy for constructing high-performance photoactive materials from MOFs-derivate.

In-situ preparation of hollow CdCoS2 heterojunction with enhanced photocurrent response for highly photoelectrochemical sensing of organophosphorus pesticides.

In this study, a porous hollow CdCoS2(2) microsphere was synthesized based on the ZIF-67-S MOFs derived method of sulfurization reaction and calcination process. Under visible light irradiation, the resulting CdCoS2(2) composite showed a markedly enhanced photoelectrochemical (PEC) response. The photocurrent value of the CdCoS2(2) modified ITO electrode was 93-fold and 41-fold than that of CoS and CdS materials, respectively. Promoting the photo-absorption ability by internal multilight scattering/reflection was due to the porous and hollow nature of CdCoS2(2). Furthermore, obtained CdCoS2(2) heterostructure in-situ with a close contact interface could facilitate the separation/migration of photo-induced carriers. The CdCoS2(2) was also mixed with Ag nanoparticles (NPs) to further improve the PEC response. Acetylcholinesterase (AChE) as a bio-recognition molecule was immobilized on the glutaraldehyde-chitosan (GLD-CS) modified CdCoS2(2)@Ag electrode surface by cross-linking effect. AChE could hydrolyze the acetylcholine chloride (ATCl) to produce an electron donor of thiocholine which led to the elevated photocurrent output. When the bioactivity of AChE was inhibited by the organophosphate pesticides (chlorpyrifos as substrate), the reduced production of thiocholine resulted in a decline in photocurrent. Under optimal conditions, the structured AChE/GLD-CS/CdCoS2(2)@Ag/ITO sensing platform was successfully achieved for chlorpyrifos detection. The wide linear response range was from 0.001 to 270 µg mL-1 and with a low detection limit of 0.57 ng mL-1. The proposed PEC biosensor also exhibited excellent selectivity and good stability, demonstrating the designed porous hollow CdCoS2(2)@Ag heterostructured composite promised to be a great application in the PEC sensors.

In-situ construction of hollow double-shelled CoSx@CdS nanocages with prominent photoelectric response for highly sensitive photoelectrochemical biosensor.

In this work, we demonstrate a delicate design and construction of hollow double-shelled CoSx@CdS nanocages (CoSx@CdS-HDSNCs) as an efficient and stable photoactive material of photoelectrochemical (PEC) biosensor for detecting cardiac troponin I (cTnI). The novel self-templated strategy started with ZIF-67, in which two distinct sulfide semiconductors were integrated into a hollow heterojunction with uniform interfacial contacts after sequential anion and cation exchange. The unique thin double shell hollow structure, suitable energy band arrangement and stable electron transmission vastly enhanced the ability of light capture and photogenerated electron-hole separation of biosensor. Subsequently, the photoelectric performance of the heterojunction was further enhanced by the deposition of Au nanoparticles (NPs) on the surface of the CoSx@CdS-HDSNCs resulting in surface plasmon resonance (SPR) effect. Based on the excellent CoSx@CdS-HDSNCs, the biosensor exhibits a high sensitivity for detection of cTnI with a wide linear range (0.00016-16 ng mL-1) and low detection limit (38.6 fg mL-1). Besides, the PEC biosensor exhibited satisfactory stability, selectivity, and reproducibility in human serum. And more importantly, our work may provide more unique inspiration for the design of photoactive materials for the future PEC sensing applications.

Application of an accelerometer in adjusting parameters and detecting characteristics in the pacemaker rate response.

Objective.To study the application of an accelerometer in adjusting the parameters, setting the sensor indicated rate (SIR) and detecting characteristics in the pacemaker (PM) rate response.Approach.Three-axis (GT9X Link-type) accelerometers were positioned on the waist and chest in 33 participants implanted with rate responsive PMs while wearing an ambulatory ECG recorder (Holter). During the walking test, by collecting vertical axis (Axis-1) activity intensity counts, Axis-1' metabolic equivalent of energy (METaxis-1) and its expected heart rate (HRmet-axis1) were calculated by the relevant equations, and on the basis of the HRmet-axis1as the target heart rate, the SIR was set by programming the rate response slope parameter. During the following daily walking activity, the physical activity parameters and Holter ECG was recorded continuously. After the end of the whole test the analysis on these data recorded was performed retrospectively.Main results.After completing the SIR setting, in 24 participants with complete ventricular pacing the comparison between HRmet-axis1(92.5 ± 7.8 BPM) and the HRvp-Holter(94.0 ± 10.5 BPM) showed no statistical difference (ΔHR: 1.25 ± 6.69 BPM,P: 0.568) during the last one walking test, and there was also no significant difference (ΔHR: 2.8 ± 7.1 BPM,P: 0.398) between the HRmet-axis1(90.7 ± 7.1 BPM) and HRvp-Holter(93.4 ± 10.3 BPM) during daily walking activity. In addition, in the data of 108 time intervals selected during the daily walking activities in the abovementioned 24 participants, METaxis-1and HRvp-Holtercorrelation analysis showed good correlation and the regression equation was HR = 12.4 × MET±43.1 (P<0.0001).Significance.An accelerometer can play an important role in adjusting parameters, setting the SIR and detecting characteristics in the PM rate response.

Sensitive photoelectrochemical detection of colitoxin DNA based on NCDs@CuO/ZnO heterostructured nanocomposites with efficient separation capacity of photo-induced carriers.

A metal-organic framework (MOF) of Cu-TPA (terephthalic acid) microsphere was prepared, followed by calcinating the MOF precursor of Cu-TPA/ZIF-8 mixture to obtain the CuO/ZnO. N-doped carbon dots (NCDs) were employed to combine the CuO/ZnO composite to form a tripartite heterostructured architecture of NCDs@CuO/ZnO, which led to a fierce enlargement of the photocurrent response. This  was ascribed to the thinner-shell structure of the CuO microsphere and the fact that hollow ZnO particles could sharply promote the incidence intensity of visible light. The more porous defectiveness exposed on CuO/ZnO surface was in favor of rapidly infiltrating electrolyte ions. The p-n type CuO/ZnO composite with more contact interface could abridge the transfer distance of photo-induced electron (e-1)/hole (h+) pairs and repress their recombination availably. NCDs not only could boost electron transfer rate on the electrode interface but also successfully sensitized the CuO/ZnO composite, which resulted in high conversion efficiency of photon-to-electron. The probe DNA (S1) was firmly assembled on the modified ITO electrode surface (S1/NCDs@CuO/ZnO) through an amidation reaction. Under optimal conditions, the prepared DNA biosensor displayed a wide linear range of 1.0 × 10-6 ~ 7.5 × 10-1 nM and a low limit of detection (LOD) of 1.81 × 10-7 nM for colitoxin DNA (S2) measure, which exhibited a better photoelectrochemistry (PEC) analysis performance than that obtained by differential pulse voltammetry techniques. The relative standard deviation (RSD) of the sensing platform for target DNA detection of 5.0 × 10-2 nM was 6.3%. This proposed DNA biosensor also showed good selectivity, stability, and reproducibility, demonstrating that the well-designed and synthesized photoactive materials of NCDs@CuO/ZnO are promising candidates for PEC analysis.